Overview

Dataset statistics

Number of variables25
Number of observations112
Missing cells960
Missing cells (%)34.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 KiB
Average record size in memory201.1 B

Variable types

Numeric22
Categorical3

Alerts

Experimental_Year is highly overall correlated with Date and 1 other fieldsHigh correlation
Dry_Weight is highly overall correlated with Crop and 1 other fieldsHigh correlation
AMF_Colonization is highly overall correlated with Crop and 1 other fieldsHigh correlation
Total_Antioxidants is highly overall correlated with AsparagineHigh correlation
Abscisic_Acid is highly overall correlated with Date and 1 other fieldsHigh correlation
Gibberellic_Acid is highly overall correlated with Date and 1 other fieldsHigh correlation
Indole_Acetic_Acid is highly overall correlated with Cytokinine and 2 other fieldsHigh correlation
Jasmonic_Acid is highly overall correlated with Salicylic_Acid and 2 other fieldsHigh correlation
Salicylic_Acid is highly overall correlated with Jasmonic_Acid and 2 other fieldsHigh correlation
Succinic_Acid is highly overall correlated with Date and 2 other fieldsHigh correlation
Asparagine is highly overall correlated with Total_Antioxidants and 3 other fieldsHigh correlation
Cytokinine is highly overall correlated with Indole_Acetic_Acid and 2 other fieldsHigh correlation
Glycine_Betaine is highly overall correlated with Date and 1 other fieldsHigh correlation
Hydrogen_Peroxid is highly overall correlated with Date and 1 other fieldsHigh correlation
Proline is highly overall correlated with CropHigh correlation
Superoxide_Dismutase is highly overall correlated with Date and 1 other fieldsHigh correlation
Trehalose is highly overall correlated with Date and 2 other fieldsHigh correlation
Tryptophan is highly overall correlated with Date and 2 other fieldsHigh correlation
Date is highly overall correlated with Experimental_Year and 12 other fieldsHigh correlation
Crop is highly overall correlated with Experimental_Year and 17 other fieldsHigh correlation
Beneficials is highly overall correlated with Dry_Weight and 7 other fieldsHigh correlation
Beneficials is highly imbalanced (62.9%)Imbalance
Dry_Weight has 16 (14.3%) missing valuesMissing
AMF_Colonization has 16 (14.3%) missing valuesMissing
Abscisic_Acid has 96 (85.7%) missing valuesMissing
Gibberellic_Acid has 96 (85.7%) missing valuesMissing
Indole_Acetic_Acid has 96 (85.7%) missing valuesMissing
Succinic_Acid has 64 (57.1%) missing valuesMissing
Asparagine has 64 (57.1%) missing valuesMissing
Cytokinine has 96 (85.7%) missing valuesMissing
Glycine_Betaine has 96 (85.7%) missing valuesMissing
Hydrogen_Peroxid has 96 (85.7%) missing valuesMissing
Superoxide_Dismutase has 96 (85.7%) missing valuesMissing
Trehalose has 64 (57.1%) missing valuesMissing
Tryptophan has 64 (57.1%) missing valuesMissing
Experimental_Year is highly skewed (γ1 = 2.069055759)Skewed
Gibberellic_Acid is highly skewed (γ1 = 1.418971963)Skewed
Jasmonic_Acid is highly skewed (γ1 = 2.635160934)Skewed
Salicylic_Acid is highly skewed (γ1 = 2.892366056)Skewed

Reproduction

Analysis started2024-07-17 12:48:25.860481
Analysis finished2024-07-17 12:49:33.870428
Duration1 minute and 8.01 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Experimental_Year
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness2.07
Mean2.02 × 103
Minimum2.02 × 103
Maximum2.02 × 103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:34.252497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.02 × 103
5-th percentile2.02 × 103
Q12.02 × 103
median2.02 × 103
Q32.02 × 103
95-th percentile2.02 × 103
Maximum2.02 × 103
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.351
Coefficient of variation (CV)0.000174
Kurtosis2.32
Mean2.02 × 103
Median Absolute Deviation (MAD)0
Skewness2.07
Sum2.26 × 105
Variance0.124
MonotonicityIncreasing
2024-07-17T14:49:34.377590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2019 96
85.7%
2020 16
 
14.3%
ValueCountFrequency (%)
2019 96
85.7%
2020 16
 
14.3%
ValueCountFrequency (%)
2020 16
 
14.3%
2019 96
85.7%

Date
Categorical

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2019-05-22 00:00:00.0000000
48 
2019-05-21 00:00:00.0000000
48 
2020-07-15 00:00:00.0000000
16 

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters3024
Distinct characters10
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-05-22 00:00:00.0000000
2nd row2019-05-22 00:00:00.0000000
3rd row2019-05-22 00:00:00.0000000
4th row2019-05-22 00:00:00.0000000
5th row2019-05-22 00:00:00.0000000

Common Values

ValueCountFrequency (%)
2019-05-22 00:00:00.0000000 48
42.9%
2019-05-21 00:00:00.0000000 48
42.9%
2020-07-15 00:00:00.0000000 16
 
14.3%

Length

2024-07-17T14:49:34.519181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T14:49:34.663172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00.0000000 112
50.0%
2019-05-22 48
21.4%
2019-05-21 48
21.4%
2020-07-15 16
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 1696
56.1%
2 272
 
9.0%
- 224
 
7.4%
: 224
 
7.4%
1 160
 
5.3%
5 112
 
3.7%
112
 
3.7%
. 112
 
3.7%
9 96
 
3.2%
7 16
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2352
77.8%
Other Punctuation 336
 
11.1%
Dash Punctuation 224
 
7.4%
Space Separator 112
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1696
72.1%
2 272
 
11.6%
1 160
 
6.8%
5 112
 
4.8%
9 96
 
4.1%
7 16
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 224
66.7%
. 112
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 224
100.0%
Space Separator
ValueCountFrequency (%)
112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3024
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1696
56.1%
2 272
 
9.0%
- 224
 
7.4%
: 224
 
7.4%
1 160
 
5.3%
5 112
 
3.7%
112
 
3.7%
. 112
 
3.7%
9 96
 
3.2%
7 16
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1696
56.1%
2 272
 
9.0%
- 224
 
7.4%
: 224
 
7.4%
1 160
 
5.3%
5 112
 
3.7%
112
 
3.7%
. 112
 
3.7%
9 96
 
3.2%
7 16
 
0.5%

Plot_ID
Real number (ℝ)

Distinct96
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.0542
Mean108
Minimum1
Maximum224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:34.791357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q156.8
median92.5
Q3164
95-th percentile218
Maximum224
Range223
Interquartile range (IQR)108

Descriptive statistics

Standard deviation68.9
Coefficient of variation (CV)0.636
Kurtosis-1.12
Mean108
Median Absolute Deviation (MAD)60
Skewness0.0542
Sum1.21 × 104
Variance4.75 × 103
MonotonicityNot monotonic
2024-07-17T14:49:34.906671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
81 3
 
2.7%
162 3
 
2.7%
5 3
 
2.7%
85 3
 
2.7%
86 3
 
2.7%
82 3
 
2.7%
6 3
 
2.7%
161 3
 
2.7%
96 1
 
0.9%
211 1
 
0.9%
Other values (86) 86
76.8%
ValueCountFrequency (%)
1 1
 
0.9%
2 1
 
0.9%
3 1
 
0.9%
4 1
 
0.9%
5 3
2.7%
ValueCountFrequency (%)
224 1
0.9%
223 1
0.9%
222 1
0.9%
221 1
0.9%
220 1
0.9%

Crop
Categorical

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Winter wheat
96 
Grain maize
16 

Length

Max length12
Median length12
Mean length11.9
Min length11

Characters and Unicode

Total characters1328
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter wheat
2nd rowWinter wheat
3rd rowWinter wheat
4th rowWinter wheat
5th rowWinter wheat

Common Values

ValueCountFrequency (%)
Winter wheat 96
85.7%
Grain maize 16
 
14.3%

Length

2024-07-17T14:49:35.038879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T14:49:35.182729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
winter 96
42.9%
wheat 96
42.9%
grain 16
 
7.1%
maize 16
 
7.1%

Most occurring characters

ValueCountFrequency (%)
e 208
15.7%
t 192
14.5%
i 128
9.6%
a 128
9.6%
n 112
8.4%
r 112
8.4%
112
8.4%
W 96
7.2%
w 96
7.2%
h 96
7.2%
Other values (3) 48
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1104
83.1%
Space Separator 112
 
8.4%
Uppercase Letter 112
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 208
18.8%
t 192
17.4%
i 128
11.6%
a 128
11.6%
n 112
10.1%
r 112
10.1%
w 96
8.7%
h 96
8.7%
m 16
 
1.4%
z 16
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
W 96
85.7%
G 16
 
14.3%
Space Separator
ValueCountFrequency (%)
112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1216
91.6%
Common 112
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 208
17.1%
t 192
15.8%
i 128
10.5%
a 128
10.5%
n 112
9.2%
r 112
9.2%
W 96
7.9%
w 96
7.9%
h 96
7.9%
G 16
 
1.3%
Other values (2) 32
 
2.6%
Common
ValueCountFrequency (%)
112
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 208
15.7%
t 192
14.5%
i 128
9.6%
a 128
9.6%
n 112
8.4%
r 112
8.4%
112
8.4%
W 96
7.2%
w 96
7.2%
h 96
7.2%
Other values (3) 48
 
3.6%

Beneficials
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Control
104 
BMs
 
8

Length

Max length7
Median length7
Mean length6.71
Min length3

Characters and Unicode

Total characters752
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowControl
2nd rowControl
3rd rowControl
4th rowControl
5th rowControl

Common Values

ValueCountFrequency (%)
Control 104
92.9%
BMs 8
 
7.1%

Length

2024-07-17T14:49:35.314024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T14:49:35.468357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
control 104
92.9%
bms 8
 
7.1%

Most occurring characters

ValueCountFrequency (%)
o 208
27.7%
C 104
13.8%
n 104
13.8%
t 104
13.8%
r 104
13.8%
l 104
13.8%
B 8
 
1.1%
M 8
 
1.1%
s 8
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 632
84.0%
Uppercase Letter 120
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 208
32.9%
n 104
16.5%
t 104
16.5%
r 104
16.5%
l 104
16.5%
s 8
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
C 104
86.7%
B 8
 
6.7%
M 8
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 208
27.7%
C 104
13.8%
n 104
13.8%
t 104
13.8%
r 104
13.8%
l 104
13.8%
B 8
 
1.1%
M 8
 
1.1%
s 8
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 208
27.7%
C 104
13.8%
n 104
13.8%
t 104
13.8%
r 104
13.8%
l 104
13.8%
B 8
 
1.1%
M 8
 
1.1%
s 8
 
1.1%

Root_Length
Real number (ℝ)

Distinct48
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.185
Mean5.85 × 103
Minimum1.12 × 103
Maximum1.27 × 104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:35.565990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.12 × 103
5-th percentile1.43 × 103
Q12.49 × 103
median5.65 × 103
Q38.59 × 103
95-th percentile1.1 × 104
Maximum1.27 × 104
Range1.16 × 104
Interquartile range (IQR)6.1 × 103

Descriptive statistics

Standard deviation3.33 × 103
Coefficient of variation (CV)0.57
Kurtosis-1.17
Mean5.85 × 103
Median Absolute Deviation (MAD)2.98 × 103
Skewness0.185
Sum6.55 × 105
Variance1.11 × 107
MonotonicityNot monotonic
2024-07-17T14:49:35.694438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2672.65 3
 
2.7%
2027.09 3
 
2.7%
7698.45 3
 
2.7%
8632.12 3
 
2.7%
12722.65 3
 
2.7%
5392.4 3
 
2.7%
6658.14 3
 
2.7%
10573.28 3
 
2.7%
6100.08 3
 
2.7%
2319.78 3
 
2.7%
Other values (38) 82
73.2%
ValueCountFrequency (%)
1120.3159 1
0.9%
1227.2624 1
0.9%
1264.1659 1
0.9%
1288.6518 1
0.9%
1314.0232 1
0.9%
ValueCountFrequency (%)
12722.65 3
2.7%
11396.96 3
2.7%
10588.38 3
2.7%
10573.28 3
2.7%
10125.23 3
2.7%

Dry_Weight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)27.1%
Missing16
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Skewness0.603
Mean11.1
Minimum9.1
Maximum14.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:35.907768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.1
5-th percentile9.2
Q110.2
median10.8
Q311.8
95-th percentile13.7
Maximum14.1
Range5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.24
Coefficient of variation (CV)0.112
Kurtosis-0.159
Mean11.1
Median Absolute Deviation (MAD)0.85
Skewness0.603
Sum1.06 × 103
Variance1.55
MonotonicityNot monotonic
2024-07-17T14:49:36.041061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10.8 9
 
8.0%
10.2 6
 
5.4%
12.8 6
 
5.4%
11.8 6
 
5.4%
10.3 6
 
5.4%
14.1 3
 
2.7%
13.7 3
 
2.7%
12.3 3
 
2.7%
11.1 3
 
2.7%
9.2 3
 
2.7%
Other values (16) 48
42.9%
(Missing) 16
 
14.3%
ValueCountFrequency (%)
9.1 3
2.7%
9.2 3
2.7%
9.5 3
2.7%
9.6 3
2.7%
9.7 3
2.7%
ValueCountFrequency (%)
14.1 3
2.7%
13.7 3
2.7%
12.8 6
5.4%
12.7 3
2.7%
12.3 3
2.7%

AMF_Colonization
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)33.3%
Missing16
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Skewness0.328
Mean49.1
Minimum26.4
Maximum71.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:36.176542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum26.4
5-th percentile33.4
Q139.5
median47.7
Q357.7
95-th percentile69.9
Maximum71.8
Range45.4
Interquartile range (IQR)18.2

Descriptive statistics

Standard deviation12
Coefficient of variation (CV)0.244
Kurtosis-0.786
Mean49.1
Median Absolute Deviation (MAD)9.44
Skewness0.328
Sum4.71 × 103
Variance143
MonotonicityNot monotonic
2024-07-17T14:49:36.298378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
37.53 3
 
2.7%
47.75 3
 
2.7%
52.23 3
 
2.7%
71.8 3
 
2.7%
36.68 3
 
2.7%
42.75 3
 
2.7%
61.55 3
 
2.7%
40.08 3
 
2.7%
36.52 3
 
2.7%
50.71 3
 
2.7%
Other values (22) 66
58.9%
(Missing) 16
 
14.3%
ValueCountFrequency (%)
26.41 3
2.7%
33.44 3
2.7%
34.64 3
2.7%
36.318 3
2.7%
36.52 3
2.7%
ValueCountFrequency (%)
71.8 3
2.7%
69.87 3
2.7%
69.7 3
2.7%
69.54 3
2.7%
62.52 3
2.7%

Total_Antioxidants
Real number (ℝ)

Distinct48
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.675
Mean68
Minimum50.8
Maximum92.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:36.429556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50.8
5-th percentile52.2
Q158.1
median63.1
Q383.5
95-th percentile91.6
Maximum92.7
Range41.9
Interquartile range (IQR)25.3

Descriptive statistics

Standard deviation13.3
Coefficient of variation (CV)0.196
Kurtosis-1.06
Mean68
Median Absolute Deviation (MAD)6.75
Skewness0.675
Sum7.62 × 103
Variance178
MonotonicityNot monotonic
2024-07-17T14:49:36.545211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
59.45 3
 
2.7%
87.69 3
 
2.7%
62.02 3
 
2.7%
66.15 3
 
2.7%
52.9 3
 
2.7%
63.29 3
 
2.7%
50.81 3
 
2.7%
55.63 3
 
2.7%
52.25 3
 
2.7%
63.18 3
 
2.7%
Other values (38) 82
73.2%
ValueCountFrequency (%)
50.81 3
2.7%
52.15 3
2.7%
52.25 3
2.7%
52.9 3
2.7%
55.63 3
2.7%
ValueCountFrequency (%)
92.68 3
2.7%
91.79 3
2.7%
91.361 1
 
0.9%
90.89 3
2.7%
89.156 1
 
0.9%

Abscisic_Acid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing96
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Skewness0.116
Mean10.4
Minimum8.86
Maximum12.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:36.669360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.86
5-th percentile9.13
Q19.6
median10.4
Q311.2
95-th percentile11.8
Maximum12.1
Range3.19
Interquartile range (IQR)1.56

Descriptive statistics

Standard deviation1.01
Coefficient of variation (CV)0.0975
Kurtosis-1.31
Mean10.4
Median Absolute Deviation (MAD)0.738
Skewness0.116
Sum166
Variance1.03
MonotonicityNot monotonic
2024-07-17T14:49:36.789399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9.77 1
 
0.9%
11.163 1
 
0.9%
9.698 1
 
0.9%
11.737 1
 
0.9%
9.765 1
 
0.9%
8.864 1
 
0.9%
10.628 1
 
0.9%
9.216 1
 
0.9%
11.637 1
 
0.9%
9.32 1
 
0.9%
Other values (6) 6
 
5.4%
(Missing) 96
85.7%
ValueCountFrequency (%)
8.864 1
0.9%
9.216 1
0.9%
9.224 1
0.9%
9.32 1
0.9%
9.698 1
0.9%
ValueCountFrequency (%)
12.054 1
0.9%
11.737 1
0.9%
11.637 1
0.9%
11.174 1
0.9%
11.163 1
0.9%

Gibberellic_Acid
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct16
Distinct (%)100.0%
Missing96
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Skewness1.42
Mean69.6
Minimum52.9
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:36.922695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum52.9
5-th percentile53.7
Q159.2
median63.4
Q376.1
95-th percentile102
Maximum112
Range59.2
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation16.8
Coefficient of variation (CV)0.242
Kurtosis1.58
Mean69.6
Median Absolute Deviation (MAD)7.79
Skewness1.42
Sum1.11 × 103
Variance283
MonotonicityNot monotonic
2024-07-17T14:49:37.035355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
54.5 1
 
0.9%
56.71 1
 
0.9%
112.11 1
 
0.9%
64.66 1
 
0.9%
68.61 1
 
0.9%
75.34 1
 
0.9%
53.89 1
 
0.9%
78.22 1
 
0.9%
60.36 1
 
0.9%
85.9 1
 
0.9%
Other values (6) 6
 
5.4%
(Missing) 96
85.7%
ValueCountFrequency (%)
52.94 1
0.9%
53.89 1
0.9%
54.5 1
0.9%
56.71 1
0.9%
60.06 1
0.9%
ValueCountFrequency (%)
112.11 1
0.9%
98.33 1
0.9%
85.9 1
0.9%
78.22 1
0.9%
75.34 1
0.9%

Indole_Acetic_Acid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing96
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Skewness0.319
Mean31.1
Minimum17.8
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:37.165790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum17.8
5-th percentile18.4
Q121.3
median29.2
Q339.4
95-th percentile48.9
Maximum50.6
Range32.8
Interquartile range (IQR)18.1

Descriptive statistics

Standard deviation11.3
Coefficient of variation (CV)0.363
Kurtosis-1.49
Mean31.1
Median Absolute Deviation (MAD)8.94
Skewness0.319
Sum498
Variance128
MonotonicityNot monotonic
2024-07-17T14:49:37.285343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
17.77 1
 
0.9%
20.9 1
 
0.9%
40.91 1
 
0.9%
21.9 1
 
0.9%
36.08 1
 
0.9%
48.29 1
 
0.9%
21.27 1
 
0.9%
37.8 1
 
0.9%
22.32 1
 
0.9%
50.6 1
 
0.9%
Other values (6) 6
 
5.4%
(Missing) 96
85.7%
ValueCountFrequency (%)
17.77 1
0.9%
18.62 1
0.9%
20.9 1
0.9%
21.27 1
0.9%
21.3 1
0.9%
ValueCountFrequency (%)
50.6 1
0.9%
48.29 1
0.9%
40.91 1
0.9%
40.32 1
0.9%
39.04 1
0.9%

Jasmonic_Acid
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct45
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness2.64
Mean1.35
Minimum0.21
Maximum12.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:37.411483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.227
Q10.256
median0.346
Q30.532
95-th percentile7.64
Maximum12.6
Range12.4
Interquartile range (IQR)0.276

Descriptive statistics

Standard deviation2.64
Coefficient of variation (CV)1.95
Kurtosis6.16
Mean1.35
Median Absolute Deviation (MAD)0.106
Skewness2.64
Sum151
Variance6.95
MonotonicityNot monotonic
2024-07-17T14:49:37.544303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.23 6
 
5.4%
0.24 6
 
5.4%
0.54 6
 
5.4%
0.3356 3
 
2.7%
0.53 3
 
2.7%
0.39 3
 
2.7%
0.41 3
 
2.7%
0.44 3
 
2.7%
0.38 3
 
2.7%
0.21 3
 
2.7%
Other values (35) 73
65.2%
ValueCountFrequency (%)
0.21 3
2.7%
0.224 3
2.7%
0.23 6
5.4%
0.24 6
5.4%
0.242 3
2.7%
ValueCountFrequency (%)
12.61 1
0.9%
11.33 1
0.9%
10.37 1
0.9%
9.64 1
0.9%
8.37 1
0.9%

Salicylic_Acid
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct46
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness2.89
Mean8.45
Minimum0.189
Maximum93.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:37.671190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.189
5-th percentile0.198
Q10.311
median0.43
Q30.585
95-th percentile75.9
Maximum93.6
Range93.5
Interquartile range (IQR)0.273

Descriptive statistics

Standard deviation22.4
Coefficient of variation (CV)2.65
Kurtosis7.29
Mean8.45
Median Absolute Deviation (MAD)0.14
Skewness2.89
Sum947
Variance500
MonotonicityNot monotonic
2024-07-17T14:49:37.787413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.58 6
 
5.4%
0.388 6
 
5.4%
0.3645 3
 
2.7%
0.49 3
 
2.7%
0.5 3
 
2.7%
0.43 3
 
2.7%
0.44 3
 
2.7%
0.3 3
 
2.7%
0.31 3
 
2.7%
0.4385 3
 
2.7%
Other values (36) 76
67.9%
ValueCountFrequency (%)
0.1885 3
2.7%
0.197 3
2.7%
0.198 3
2.7%
0.216 3
2.7%
0.27 3
2.7%
ValueCountFrequency (%)
93.64 1
0.9%
91.5 1
0.9%
89.87 1
0.9%
87.82 1
0.9%
81.33 1
0.9%

Succinic_Acid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)20.8%
Missing64
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Skewness0.155
Mean0.181
Minimum0.12
Maximum0.249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:37.903846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.12
Q10.15
median0.185
Q30.205
95-th percentile0.249
Maximum0.249
Range0.129
Interquartile range (IQR)0.055

Descriptive statistics

Standard deviation0.0431
Coefficient of variation (CV)0.237
Kurtosis-1.2
Mean0.181
Median Absolute Deviation (MAD)0.035
Skewness0.155
Sum8.71
Variance0.00186
MonotonicityNot monotonic
2024-07-17T14:49:38.042145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.15 9
 
8.0%
0.2 9
 
8.0%
0.12 6
 
5.4%
0.249 6
 
5.4%
0.154 3
 
2.7%
0.13 3
 
2.7%
0.18 3
 
2.7%
0.24 3
 
2.7%
0.22 3
 
2.7%
0.19 3
 
2.7%
(Missing) 64
57.1%
ValueCountFrequency (%)
0.12 6
5.4%
0.13 3
 
2.7%
0.15 9
8.0%
0.154 3
 
2.7%
0.18 3
 
2.7%
ValueCountFrequency (%)
0.249 6
5.4%
0.24 3
 
2.7%
0.22 3
 
2.7%
0.2 9
8.0%
0.19 3
 
2.7%

Ascorbate_Peroxidase
Real number (ℝ)

Distinct48
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.359
Mean429
Minimum248
Maximum668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:38.174757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum248
5-th percentile252
Q1355
median404
Q3536
95-th percentile637
Maximum668
Range420
Interquartile range (IQR)181

Descriptive statistics

Standard deviation123
Coefficient of variation (CV)0.286
Kurtosis-0.877
Mean429
Median Absolute Deviation (MAD)78.5
Skewness0.359
Sum4.81 × 104
Variance1.51 × 104
MonotonicityNot monotonic
2024-07-17T14:49:38.287200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
450.06 3
 
2.7%
623.39 3
 
2.7%
455.01 3
 
2.7%
411.15 3
 
2.7%
403.87 3
 
2.7%
393.05 3
 
2.7%
355.05 3
 
2.7%
381.15 3
 
2.7%
383.29 3
 
2.7%
433.05 3
 
2.7%
Other values (38) 82
73.2%
ValueCountFrequency (%)
247.59 3
2.7%
250.02 3
2.7%
253.07 3
2.7%
261.1 3
2.7%
268.25 3
2.7%
ValueCountFrequency (%)
668.01 3
2.7%
642.27 3
2.7%
633.29 3
2.7%
623.39 3
2.7%
619.532 1
 
0.9%

Asparagine
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)25.0%
Missing64
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Skewness-0.0137
Mean0.664
Minimum0.47
Maximum0.849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:38.407582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.47
5-th percentile0.472
Q10.508
median0.669
Q30.83
95-th percentile0.848
Maximum0.849
Range0.379
Interquartile range (IQR)0.322

Descriptive statistics

Standard deviation0.168
Coefficient of variation (CV)0.252
Kurtosis-2.04
Mean0.664
Median Absolute Deviation (MAD)0.16
Skewness-0.0137
Sum31.9
Variance0.0281
MonotonicityNot monotonic
2024-07-17T14:49:38.536450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.83 12
 
10.7%
0.51 6
 
5.4%
0.47 3
 
2.7%
0.5 3
 
2.7%
0.475 3
 
2.7%
0.53 3
 
2.7%
0.52 3
 
2.7%
0.48 3
 
2.7%
0.809 3
 
2.7%
0.847 3
 
2.7%
Other values (2) 6
 
5.4%
(Missing) 64
57.1%
ValueCountFrequency (%)
0.47 3
2.7%
0.475 3
2.7%
0.48 3
2.7%
0.5 3
2.7%
0.51 6
5.4%
ValueCountFrequency (%)
0.849 3
 
2.7%
0.847 3
 
2.7%
0.83 12
10.7%
0.81 3
 
2.7%
0.809 3
 
2.7%

Cytokinine
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing96
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Skewness0.448
Mean48.1
Minimum25.8
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:38.676982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum25.8
5-th percentile27.7
Q130.9
median43.2
Q360.7
95-th percentile78.1
Maximum81
Range55.2
Interquartile range (IQR)29.8

Descriptive statistics

Standard deviation19.4
Coefficient of variation (CV)0.403
Kurtosis-1.41
Mean48.1
Median Absolute Deviation (MAD)13
Skewness0.448
Sum770
Variance376
MonotonicityNot monotonic
2024-07-17T14:49:38.789284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
25.83 1
 
0.9%
30.37 1
 
0.9%
72.34 1
 
0.9%
33.37 1
 
0.9%
52.42 1
 
0.9%
70.17 1
 
0.9%
30.92 1
 
0.9%
57.6 1
 
0.9%
33.88 1
 
0.9%
77.1 1
 
0.9%
Other values (6) 6
 
5.4%
(Missing) 96
85.7%
ValueCountFrequency (%)
25.83 1
0.9%
28.38 1
0.9%
30.37 1
0.9%
30.84 1
0.9%
30.92 1
0.9%
ValueCountFrequency (%)
80.99 1
0.9%
77.1 1
0.9%
72.34 1
0.9%
70.17 1
0.9%
57.6 1
0.9%

Glycine_Betaine
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing96
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Skewness0.865
Mean7.75
Minimum5.84
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:38.909587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.84
5-th percentile6.15
Q16.65
median7.43
Q38.33
95-th percentile10.4
Maximum11
Range5.2
Interquartile range (IQR)1.68

Descriptive statistics

Standard deviation1.47
Coefficient of variation (CV)0.189
Kurtosis0.265
Mean7.75
Median Absolute Deviation (MAD)0.902
Skewness0.865
Sum124
Variance2.15
MonotonicityNot monotonic
2024-07-17T14:49:39.035078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6.521 1
 
0.9%
6.693 1
 
0.9%
7.7486 1
 
0.9%
5.8413 1
 
0.9%
9.1204 1
 
0.9%
8.3033 1
 
0.9%
6.8754 1
 
0.9%
8.286 1
 
0.9%
8.3252 1
 
0.9%
8.34074 1
 
0.9%
Other values (6) 6
 
5.4%
(Missing) 96
85.7%
ValueCountFrequency (%)
5.8413 1
0.9%
6.2463 1
0.9%
6.2876 1
0.9%
6.521 1
0.9%
6.693 1
0.9%
ValueCountFrequency (%)
11.04372 1
0.9%
10.2059 1
0.9%
9.1204 1
0.9%
8.34074 1
0.9%
8.3252 1
0.9%

Hydrogen_Peroxid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing96
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Skewness0.0981
Mean56.3
Minimum29.1
Maximum86.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:39.156736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum29.1
5-th percentile33.6
Q137.5
median52.7
Q377.5
95-th percentile81.1
Maximum86.8
Range57.7
Interquartile range (IQR)40

Descriptive statistics

Standard deviation21.2
Coefficient of variation (CV)0.377
Kurtosis-2
Mean56.3
Median Absolute Deviation (MAD)17.6
Skewness0.0981
Sum902
Variance450
MonotonicityNot monotonic
2024-07-17T14:49:39.272397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
79.162 1
 
0.9%
77.253 1
 
0.9%
35.169 1
 
0.9%
78.196 1
 
0.9%
35.139 1
 
0.9%
38.385 1
 
0.9%
65.6 1
 
0.9%
36.478 1
 
0.9%
79.156 1
 
0.9%
39.847 1
 
0.9%
Other values (6) 6
 
5.4%
(Missing) 96
85.7%
ValueCountFrequency (%)
29.128 1
0.9%
35.139 1
0.9%
35.169 1
0.9%
36.478 1
0.9%
37.878 1
0.9%
ValueCountFrequency (%)
86.785 1
0.9%
79.162 1
0.9%
79.156 1
0.9%
78.196 1
0.9%
77.253 1
0.9%

Proline
Real number (ℝ)

Distinct44
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness-0.117
Mean1.73
Minimum0.149
Maximum3.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:39.399178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.149
5-th percentile0.19
Q11.29
median1.67
Q32.32
95-th percentile3.28
Maximum3.41
Range3.26
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation0.878
Coefficient of variation (CV)0.506
Kurtosis-0.44
Mean1.73
Median Absolute Deviation (MAD)0.58
Skewness-0.117
Sum194
Variance0.771
MonotonicityNot monotonic
2024-07-17T14:49:39.520502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.32 9
 
8.0%
2.61 6
 
5.4%
1.99 6
 
5.4%
1.65 3
 
2.7%
2.16 3
 
2.7%
2.06 3
 
2.7%
1.43 3
 
2.7%
1.67 3
 
2.7%
1.58 3
 
2.7%
1.62 3
 
2.7%
Other values (34) 70
62.5%
ValueCountFrequency (%)
0.1485424149 1
0.9%
0.1528 1
0.9%
0.1783869969 1
0.9%
0.1799424149 1
0.9%
0.1835 1
0.9%
ValueCountFrequency (%)
3.41 3
2.7%
3.39 3
2.7%
3.19 3
2.7%
2.99 3
2.7%
2.72 3
2.7%

Polyphenols
Real number (ℝ)

Distinct44
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.399
Mean3.84
Minimum2.08
Maximum7.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:39.661980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.08
5-th percentile2.2
Q12.48
median3.65
Q35.07
95-th percentile6.28
Maximum7.16
Range5.08
Interquartile range (IQR)2.59

Descriptive statistics

Standard deviation1.46
Coefficient of variation (CV)0.379
Kurtosis-1.18
Mean3.84
Median Absolute Deviation (MAD)1.24
Skewness0.399
Sum430
Variance2.12
MonotonicityNot monotonic
2024-07-17T14:49:39.804529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.49 6
 
5.4%
5.06 6
 
5.4%
2.08 3
 
2.7%
4.62 3
 
2.7%
3.65 3
 
2.7%
3.31 3
 
2.7%
3.88 3
 
2.7%
3.69 3
 
2.7%
5.07 3
 
2.7%
5.16 3
 
2.7%
Other values (34) 76
67.9%
ValueCountFrequency (%)
2.08 3
2.7%
2.19 3
2.7%
2.2 3
2.7%
2.31 3
2.7%
2.341 1
 
0.9%
ValueCountFrequency (%)
7.158 2
1.8%
6.765 2
1.8%
6.697 1
0.9%
6.329 1
0.9%
6.235 1
0.9%

Superoxide_Dismutase
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing96
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Skewness0.116
Mean180
Minimum101
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:39.933803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile104
Q1119
median166
Q3253
95-th percentile264
Maximum265
Range164
Interquartile range (IQR)134

Descriptive statistics

Standard deviation69
Coefficient of variation (CV)0.383
Kurtosis-2.08
Mean180
Median Absolute Deviation (MAD)55.9
Skewness0.116
Sum2.89 × 103
Variance4.77 × 103
MonotonicityNot monotonic
2024-07-17T14:49:40.068852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
114.813 1
 
0.9%
101.092 1
 
0.9%
265.118 1
 
0.9%
105.487 1
 
0.9%
252.439 1
 
0.9%
206.497 1
 
0.9%
115.151 1
 
0.9%
263.415 1
 
0.9%
125.697 1
 
0.9%
215.475 1
 
0.9%
Other values (6) 6
 
5.4%
(Missing) 96
85.7%
ValueCountFrequency (%)
101.092 1
0.9%
105.487 1
0.9%
114.813 1
0.9%
115.151 1
0.9%
119.805 1
0.9%
ValueCountFrequency (%)
265.118 1
0.9%
263.415 1
0.9%
258.198 1
0.9%
254.071 1
0.9%
252.439 1
0.9%

Trehalose
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)31.2%
Missing64
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Skewness0.34
Mean14.7
Minimum3.42
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:40.204194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.42
5-th percentile3.66
Q15.99
median12
Q323.7
95-th percentile30.1
Maximum31.5
Range28.1
Interquartile range (IQR)17.7

Descriptive statistics

Standard deviation9.73
Coefficient of variation (CV)0.664
Kurtosis-1.5
Mean14.7
Median Absolute Deviation (MAD)7.36
Skewness0.34
Sum703
Variance94.7
MonotonicityNot monotonic
2024-07-17T14:49:40.332775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
27.38368 6
 
5.4%
5.476736 3
 
2.7%
6.161328 3
 
2.7%
3.42296 3
 
2.7%
6.84592 3
 
2.7%
8.899696 3
 
2.7%
4.107552 3
 
2.7%
4.792144 3
 
2.7%
6.503624 3
 
2.7%
24.9533784 3
 
2.7%
Other values (5) 15
 
13.4%
(Missing) 64
57.1%
ValueCountFrequency (%)
3.42296 3
2.7%
4.107552 3
2.7%
4.792144 3
2.7%
5.476736 3
2.7%
6.161328 3
2.7%
ValueCountFrequency (%)
31.491232 3
2.7%
27.38368 6
5.4%
24.9533784 3
2.7%
23.276128 3
2.7%
19.510872 3
2.7%

Tryptophan
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)29.2%
Missing64
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Skewness-0.136
Mean0.00258
Minimum0.0016
Maximum0.0034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-07-17T14:49:40.473882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0016
5-th percentile0.00167
Q10.002
median0.00265
Q30.00314
95-th percentile0.00338
Maximum0.0034
Range0.0018
Interquartile range (IQR)0.00114

Descriptive statistics

Standard deviation0.000612
Coefficient of variation (CV)0.237
Kurtosis-1.62
Mean0.00258
Median Absolute Deviation (MAD)0.00055
Skewness-0.136
Sum0.124
Variance3.75 × 10-7
MonotonicityNot monotonic
2024-07-17T14:49:40.607057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.002 6
 
5.4%
0.0032 6
 
5.4%
0.0018 3
 
2.7%
0.0023 3
 
2.7%
0.0016 3
 
2.7%
0.0019 3
 
2.7%
0.0021 3
 
2.7%
0.0024 3
 
2.7%
0.0034 3
 
2.7%
0.0031 3
 
2.7%
Other values (4) 12
 
10.7%
(Missing) 64
57.1%
ValueCountFrequency (%)
0.0016 3
2.7%
0.0018 3
2.7%
0.0019 3
2.7%
0.002 6
5.4%
0.0021 3
2.7%
ValueCountFrequency (%)
0.0034 3
2.7%
0.00333 3
2.7%
0.0032 6
5.4%
0.00312 3
2.7%
0.0031 3
2.7%

Interactions

2024-07-17T14:49:29.518239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:26.684434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:29.787678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.686621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.812512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.558743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.310475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:44.500789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:47.453376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.381612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:53.661934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:56.527657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:59.340909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.610384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:05.505069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:08.492202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:11.592836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:14.476645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:17.277467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:20.701540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.515806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:26.716073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:29.666958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:26.817562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:29.917591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.817342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.959422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.700462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.442792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:44.649051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:47.607066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.534134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:53.796181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:56.653783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:59.508513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.742903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:05.668482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:08.638930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:11.763089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:14.619311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:17.415113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:20.854017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.666864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:26.864822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:29.813944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:26.936540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:30.033642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.938153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:36.079568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.823564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.561169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:44.797398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:47.762413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.682926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:53.919224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:56.767333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:59.644913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.858685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:05.824515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:09.110108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:11.917704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:14.764117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:17.845698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:20.969736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.818483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:27.000124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:29.944215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:27.060948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:30.165129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:33.064698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:36.217841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.946528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.688300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:44.918409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:47.882609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.798007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:54.051877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:56.885811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:59.774282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.982717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:05.963372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:09.219851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:12.038027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:14.877246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:17.976841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:21.106268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.933859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:27.130556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:30.070238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:27.199946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:30.291209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:33.196736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:36.340975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:39.069755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.812636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:45.041458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:48.001556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.917155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:54.190939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:57.006193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:59.894236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:03.117696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:06.092078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:09.335508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:12.160827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:15.007355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:18.099169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:21.242556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:24.053161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:27.251471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:30.200675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:27.341039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:30.408064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:33.317872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:36.463511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:39.194291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.928924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:45.163644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:48.120131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:51.035488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:54.322228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:57.123912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:00.377178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:03.239085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:06.223371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:09.452182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:12.281194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:15.140517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:18.220881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:21.370506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:24.178316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:27.373455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:30.352931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:27.480104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:30.528713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:33.440902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:36.585945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:39.314432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:42.045118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:45.314105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:48.247204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:51.161254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:54.459076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:57.238646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:00.513525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2024-07-17T14:49:13.586801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:16.426280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:19.714760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:22.634806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:25.481883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:28.644079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:31.703428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:28.909781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:31.898194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.013811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:37.788266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:40.550031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:43.682040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:46.687769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:49.607715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:52.872733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:55.733129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:58.537656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:01.823968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:04.690104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:07.693537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:10.799982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:13.709616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:16.547048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:19.850885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:22.753224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:25.617269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:28.756840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:31.818947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:29.052552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.028342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.144559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:37.906219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:40.667340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:43.815420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:46.812564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:49.737452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:52.999183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:55.856776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:58.667711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:01.937527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:04.821901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:07.808227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:10.943463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:13.836836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:16.667459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:19.982819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:22.875360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:26.072934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:28.874118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:31.972557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:29.199416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.170318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.281018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.040540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:40.796143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:43.962874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:46.945549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:49.883170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:53.134831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:55.998681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:58.801245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.081444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:04.963218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:07.960588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:11.091310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:13.972724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:16.799752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:20.125293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.016359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:26.213265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:29.016246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:32.104330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:29.334579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.287916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.416356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.184692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:40.931596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:44.095872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:47.062311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.011088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:53.257489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:56.123430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:58.923475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.209861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:05.095793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:08.083786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:11.207863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:14.090103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:16.918791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:20.255377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.137494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:26.341108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:29.144529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:32.225074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:29.487280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.427844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.539051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.302746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.051956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:44.235014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:47.202657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.140941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:53.411537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:56.252248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:59.060138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.329015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:05.226314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:08.203997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:11.342219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:14.233155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:17.049590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:20.395807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.263204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:26.474985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:29.259354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:32.361139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:29.632749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:32.548711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:35.673151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:38.424639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:41.172490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:44.365459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:47.329673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:50.251895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:53.530949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:56.386494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:48:59.200119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:02.467273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:05.361594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:08.338852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:11.466064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:14.355001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:17.165359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:20.549401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:23.382224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:26.596782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:49:29.380848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-07-17T14:49:40.823142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Experimental_YearPlot_IDRoot_LengthDry_WeightAMF_ColonizationTotal_AntioxidantsAbscisic_AcidGibberellic_AcidIndole_Acetic_AcidJasmonic_AcidSalicylic_AcidSuccinic_AcidAscorbate_PeroxidaseAsparagineCytokinineGlycine_BetaineHydrogen_PeroxidProlinePolyphenolsSuperoxide_DismutaseTrehaloseTryptophanDateCropBeneficials
Experimental_Year1.000-0.164-0.606NaNNaN0.372NaNNaNNaN0.6060.606NaN0.206NaNNaNNaNNaN-0.6060.391NaNNaNNaN0.9950.9630.625
Plot_ID-0.1641.0000.078-0.0210.063-0.169-0.2540.278-0.107-0.182-0.191-0.082-0.1650.027-0.107-0.213-0.047-0.001-0.288-0.130-0.106-0.1050.7030.3840.184
Root_Length-0.6060.0781.0000.6350.598-0.281-0.0620.2850.282-0.388-0.3800.397-0.2740.3910.3530.218-0.0650.378-0.1490.4030.3000.2680.6280.7790.486
Dry_WeightNaN-0.0210.6351.0000.2990.111NaNNaNNaN0.0430.0790.201-0.0250.148NaNNaNNaN0.105-0.047NaN0.4240.1360.3831.0001.000
AMF_ColonizationNaN0.0630.5980.2991.000-0.199NaNNaNNaN-0.112-0.1260.307-0.3930.362NaNNaNNaN-0.145-0.129NaN0.2530.2670.3691.0001.000
Total_Antioxidants0.372-0.169-0.2810.111-0.1991.000-0.5620.7350.7650.7550.768-0.7630.749-0.9100.8240.624-0.7380.2760.4950.809-0.693-0.7300.6460.6620.372
Abscisic_AcidNaN-0.254-0.062NaNNaN-0.5621.000-0.565-0.4210.5030.641NaN-0.388NaN-0.326-0.3260.4320.315-0.059-0.450NaNNaN1.0001.0000.231
Gibberellic_AcidNaN0.2780.285NaNNaN0.735-0.5651.0000.768-0.565-0.697NaN0.691NaN0.7680.521-0.612-0.2290.2810.709NaNNaN1.0001.0000.598
Indole_Acetic_AcidNaN-0.1070.282NaNNaN0.765-0.4210.7681.000-0.838-0.835NaN0.629NaN0.9650.703-0.6680.1910.7170.779NaNNaN1.0001.0000.845
Jasmonic_Acid0.606-0.182-0.3880.043-0.1120.7550.503-0.565-0.8381.0000.912-0.7080.708-0.732-0.753-0.6970.7000.0930.448-0.603-0.721-0.7460.6600.9680.920
Salicylic_Acid0.606-0.191-0.3800.079-0.1260.7680.641-0.697-0.8350.9121.000-0.7880.755-0.701-0.803-0.6320.7150.2260.538-0.647-0.633-0.7860.6810.9820.982
Succinic_AcidNaN-0.0820.3970.2010.307-0.763NaNNaNNaN-0.708-0.7881.000-0.7840.750NaNNaNNaN-0.732-0.225NaN0.7280.7491.0001.0001.000
Ascorbate_Peroxidase0.206-0.165-0.274-0.025-0.3930.749-0.3880.6910.6290.7080.755-0.7841.000-0.8840.7150.500-0.8000.5080.5310.721-0.714-0.6570.5870.5250.781
AsparagineNaN0.0270.3910.1480.362-0.910NaNNaNNaN-0.732-0.7010.750-0.8841.000NaNNaNNaN-0.724-0.127NaN0.7880.6771.0001.0001.000
CytokinineNaN-0.1070.353NaNNaN0.824-0.3260.7680.965-0.753-0.803NaN0.715NaN1.0000.700-0.7260.1590.6630.824NaNNaN1.0001.0000.845
Glycine_BetaineNaN-0.2130.218NaNNaN0.624-0.3260.5210.703-0.697-0.632NaN0.500NaN0.7001.000-0.7380.0850.5610.744NaNNaN1.0001.0000.486
Hydrogen_PeroxidNaN-0.047-0.065NaNNaN-0.7380.432-0.612-0.6680.7000.715NaN-0.800NaN-0.726-0.7381.0000.097-0.377-0.829NaNNaN1.0001.0000.845
Proline-0.606-0.0010.3780.105-0.1450.2760.315-0.2290.1910.0930.226-0.7320.508-0.7240.1590.0850.0971.0000.124-0.088-0.645-0.7220.7880.9680.627
Polyphenols0.391-0.288-0.149-0.047-0.1290.495-0.0590.2810.7170.4480.538-0.2250.531-0.1270.6630.561-0.3770.1241.0000.518-0.340-0.2550.8370.7200.801
Superoxide_DismutaseNaN-0.1300.403NaNNaN0.809-0.4500.7090.779-0.603-0.647NaN0.721NaN0.8240.744-0.829-0.0880.5181.000NaNNaN1.0001.0000.886
TrehaloseNaN-0.1060.3000.4240.253-0.693NaNNaNNaN-0.721-0.6330.728-0.7140.788NaNNaNNaN-0.645-0.340NaN1.0000.7161.0001.0001.000
TryptophanNaN-0.1050.2680.1360.267-0.730NaNNaNNaN-0.746-0.7860.749-0.6570.677NaNNaNNaN-0.722-0.255NaN0.7161.0001.0001.0001.000
Date0.9950.7030.6280.3830.3690.6461.0001.0001.0000.6600.6811.0000.5871.0001.0001.0001.0000.7880.8371.0001.0001.0001.0000.9950.669
Crop0.9630.3840.7791.0001.0000.6621.0001.0001.0000.9680.9821.0000.5251.0001.0001.0001.0000.9680.7201.0001.0001.0000.9951.0000.625
Beneficials0.6250.1840.4861.0001.0000.3720.2310.5980.8450.9200.9821.0000.7811.0000.8450.4860.8450.6270.8010.8861.0001.0000.6690.6251.000

Missing values

2024-07-17T14:49:32.634346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-17T14:49:33.170845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-17T14:49:33.571492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Experimental_YearDatePlot_IDCropBeneficialsRoot_LengthDry_WeightAMF_ColonizationTotal_AntioxidantsAbscisic_AcidGibberellic_AcidIndole_Acetic_AcidJasmonic_AcidSalicylic_AcidSuccinic_AcidAscorbate_PeroxidaseAsparagineCytokinineGlycine_BetaineHydrogen_PeroxidProlinePolyphenolsSuperoxide_DismutaseTrehaloseTryptophan
020192019-05-22 00:00:00.000000051Winter wheatControl2672.6511.033.4459.45NaNNaNNaN0.33560.3645NaN450.06NaNNaNNaNNaN1.652.08NaNNaNNaN
120192019-05-22 00:00:00.000000055Winter wheatControl2319.7810.437.9363.18NaNNaNNaN0.36600.4385NaN433.05NaNNaNNaNNaN1.622.42NaNNaNNaN
220192019-05-22 00:00:00.000000059Winter wheatControl3925.369.542.9856.19NaNNaNNaN0.31400.3880NaN399.48NaNNaNNaNNaN1.802.48NaNNaNNaN
320192019-05-22 00:00:00.000000063Winter wheatControl2536.569.641.3259.14NaNNaNNaN0.34600.3880NaN414.02NaNNaNNaNNaN1.852.43NaNNaNNaN
420192019-05-22 00:00:00.000000052Winter wheatControl3529.5110.356.8163.10NaNNaNNaN0.29560.4170NaN403.30NaNNaNNaNNaN1.992.49NaNNaNNaN
520192019-05-22 00:00:00.000000056Winter wheatControl5648.7710.269.8761.06NaNNaNNaN0.27900.4680NaN382.28NaNNaNNaNNaN2.222.52NaNNaNNaN
620192019-05-22 00:00:00.000000060Winter wheatControl6783.3114.147.5658.12NaNNaNNaN0.24200.4180NaN392.40NaNNaNNaNNaN2.322.61NaNNaNNaN
720192019-05-22 00:00:00.000000064Winter wheatControl8456.9511.743.6758.13NaNNaNNaN0.22400.3900NaN345.20NaNNaNNaNNaN2.182.19NaNNaNNaN
820192019-05-22 00:00:00.000000050Winter wheatControl7199.9512.854.0657.01NaNNaNNaN0.25600.2160NaN282.07NaNNaNNaNNaN1.092.41NaNNaNNaN
920192019-05-22 00:00:00.000000054Winter wheatControl8741.3010.748.1152.15NaNNaNNaN0.25100.1970NaN253.07NaNNaNNaNNaN0.952.45NaNNaNNaN
Experimental_YearDatePlot_IDCropBeneficialsRoot_LengthDry_WeightAMF_ColonizationTotal_AntioxidantsAbscisic_AcidGibberellic_AcidIndole_Acetic_AcidJasmonic_AcidSalicylic_AcidSuccinic_AcidAscorbate_PeroxidaseAsparagineCytokinineGlycine_BetaineHydrogen_PeroxidProlinePolyphenolsSuperoxide_DismutaseTrehaloseTryptophan
10220202020-07-15 00:00:00.000000081Grain maizeBMs1581.1781NaNNaN89.1560009.32085.9050.604.6525.24NaN516.552000NaN77.108.3407439.8470.2597007.158215.475NaNNaN
10320202020-07-15 00:00:00.000000085Grain maizeBMs1560.2982NaNNaN91.3610009.21678.2237.806.4731.21NaN619.532000NaN57.608.2860036.4780.1835004.482263.415NaNNaN
10420202020-07-15 00:00:00.000000082Grain maizeControl1460.2788NaNNaN69.3366449.77054.5017.7710.3791.50NaN420.047910NaN25.836.5210079.1620.1967602.341114.813NaNNaN
10520202020-07-15 00:00:00.000000086Grain maizeControl1432.9162NaNNaN71.04857610.62853.8921.279.6471.35NaN387.005486NaN30.926.8754065.6000.1799425.121115.151NaNNaN
10620202020-07-15 00:00:00.000000082Grain maizeBMs1288.6518NaNNaN86.6717778.86475.3448.293.6924.66NaN502.158910NaN70.178.3033038.3850.2524646.765206.497NaNNaN
10720202020-07-15 00:00:00.000000086Grain maizeBMs1498.6853NaNNaN88.8153379.76568.6136.085.5130.50NaN602.269498NaN52.429.1204035.1390.1783874.236252.439NaNNaN
10820202020-07-15 00:00:00.0000000161Grain maizeControl1314.0232NaNNaN68.50700011.73764.6621.908.2781.33NaN457.617000NaN33.375.8413078.1960.1987002.982105.487NaNNaN
10920202020-07-15 00:00:00.0000000161Grain maizeBMs1724.6857NaNNaN85.6340009.698112.1140.916.1331.22NaN527.640000NaN72.347.7486035.1690.1528006.235265.118NaNNaN
11020202020-07-15 00:00:00.0000000162Grain maizeControl1227.2624NaNNaN66.59813611.16356.7120.907.0479.47NaN444.866062NaN30.376.6930077.2530.1931632.818101.092NaNNaN
11120202020-07-15 00:00:00.0000000162Grain maizeBMs1429.8556NaNNaN83.2479139.22498.3339.045.2230.51NaN512.937957NaN56.4710.2059037.8780.1485425.893254.071NaNNaN